Animal species interactions are very complex and it is a difficult task to determine how one species might affect another, or how human intervention into a population might trigger a chain of events to affect other species later on. A tool that enables researchers to visualize such interactions would be very helpful in order to determine and understand some of these relationships to be able to better enforce or design animal and forest protection policies.

Such a tool will need meaningful data, too. In the data gathering phase of our project, we were unable to find applications that focused on visualizing animal populations and their evolution over time. We also had some hard time finding data in a generic enough format that could be imported easily into a database to further analyze it. We were lucky enough to get a data set in CSV format that we could import into MySQL.

Some of the sites we found \cite{usgs, imperialcollege, cites} contained useful and vast amounts of data, however, the process of acquiring the data is tedious because it is spread out over multiple pages and would require much post processing. We think that our tool fills the gap between freely available data and its effective utilization in order to gain interesting insights.

As stated before, we had to drastically reduce the size of our working data set in order to be able to use the application in an interactive way. At this point, it was not a question of information visualization techniques, but of the sheer volume of the data and the performance of our back end. In order to get full advantage of an information visualization tool designed to understand temporal data, it must be interactive enough for the user to observe changing patterns. Despite the lack of this aspect in our tool, we were able to reach interesting conclusions. Clearly, this is the main challenge for tools that want to help users visualize fine grained data about animal populations for large contiguous areas.

After the data gathering phase of our project, we had learned that there is no shortage of data available and that it is fairly straightforward to develop a geographical visualization by means of open source components like OpenMap \cite{openmap}. Consequently, the apparent lack of tools like ours surprised us, however after trying to deal with the challenges we have described, we started to understand that the problem is not a lack of motivation or data.
